Follow Up Action Required: Check if this is appropriate
The Street Cleaning Function has 3 lines of codes that were set to be ignored. The code were:
# Lines of codes that had "##" in the Street Cleaning Function
##x<-gsub("\\bSTR\\b","", x)
##x<-gsub("\\<ST\\>","",x)
##x<-gsub("\\<STREET\\>","",x)
The combined Street Dictionary had several issues regarding “STR”, “ST” and “STREET”. This might have been the cause of those issues.
The Street Dictionary (combined and full) for Manhattan and Brooklyn had several abnormalities. From manual checking the issues that were identified included:
From the sample dataset, an output of 557,357 rows by 33 columns was derived.
There were 1,478 Enumeration Districts, 1,276 microfilms and 8,557 unique street names within the dataset.
The results from the Street Matching function were as follow:
# Distribution of Result Type in Manhattan
plot(table(mn_output$result_type),
type = "h",
col = c("blue", "red", "orange", "purple", "green", "pink"),
lwd = 10,
main = "Result Type for Manhattan",
ylab = "Count",
xlab = "Result Type")
# Sample of Result Type 1 (Perfect Match)
mn_output %>% filter(result_type == 1) %>% select(ED, street_add, best_match, result_type) %>% head(1)
# Sample of Result Type 2 (Identical Match)
mn_output %>% filter(result_type == 2) %>% select(ED, street_add, best_match, result_type) %>% head(1)
# Sample of Result Type 3 (Singular Mode)
mn_output %>% filter(result_type == 3) %>% select(ED, street_add, best_match, result_type) %>% head(1)
# Sample of Result Type 4 (Multiple Modes)
mn_output %>% filter(result_type == 4) %>% select(ED, street_add, best_match, result_type) %>% head(1)
# Sample of Result Type 1 (NA)
mn_output %>% filter(result_type == 5) %>% select(ED, street_add, best_match, result_type) %>% head(3)
# Sample of Result Type 6 (no match)
mn_output %>% filter(result_type == 6) %>% select(ED, street_add, best_match, result_type) %>% head(4)
Out of the problematic Street Matches (result type 5 and type 6), there is a trend of some EDs being more problematic than others, i.e. more entries of 5 or 6 within certain Enumeration Districts.
# Problematic EDs for Result Type 5
# Treshold set to 50 (arbitrarily decided)
mn_output %>% filter(result_type == 5) %>%
select(ED) %>% table() %>% sort(decreasing = TRUE) %>% head(29)
## .
## 0948 0836 1164 1393 0984 0365 0861 1267 0600 0288 0725 0081 0818 0049 1270 0009
## 546 351 289 288 284 225 186 182 167 150 147 113 112 100 100 92
## 0026 1392 0766 0421 0427 1679 1202 1643 0953 0394 0110 1377 1232
## 88 87 85 82 78 77 74 74 72 65 54 54 50
# Problematic EDs for Result Type 6
# Treshold set to 50 (arbitrarily decided)
mn_output %>% filter(result_type == 6) %>%
select(ED) %>% table() %>% sort(decreasing = TRUE) %>% head(6)
## .
## 0815 1481 1913 0764 0782 1941
## 1432 522 248 84 83 67
# Average result_type by ED
average_result_type_mn <- mn_output %>%
select(ED, result_type) %>%
group_by(ED) %>%
summarise(mean_result_type = mean(result_type))
average_result_type_mn$ED <- as.numeric(average_result_type_mn$ED)
mn_result_type_plot <- ggplot(average_result_type_mn,
aes(x = ED, y = mean_result_type)) +
theme_classic() +
geom_point() +
labs(x = "ED", y = "Average Result Type", title = "Mean Result Type by ED (MN)"
)
ggplotly(mn_result_type_plot)
# Standard Deviation of result_type by ED
sd_result_type_mn <- mn_output %>%
select(ED, result_type) %>%
group_by(ED) %>%
summarise(sd_result_type = sd(result_type))
sd_result_type_mn$ED <- as.numeric(sd_result_type_mn$ED)
mn_sd_result_type_plot <- ggplot(sd_result_type_mn,
aes(x = ED, y = sd_result_type)) +
theme_classic() +
geom_point() +
labs(x = "ED", y = "Standard Deviation of Result Type", title = "Standard Deviation of Result Type by ED (MN)"
)
ggplotly(mn_sd_result_type_plot)
Out of the 28% of Perfect Matches (156k entries) 1,717 entries were matched via the Fill Down function 03_Matched_Street_Fill_Down - 1.1%.
Out of 557,357 entries, 159,669 entries were flagged for house number changes (i.e. 0 or 1) - 28.6%.
7,350 entries were flagged with “1” meaning the house number was editted by the function. E.g. The initial household number might have been “195-7”, “34 TO 36” or “112 114”. There would be a split between the house numbers and hn_1 will be 195 and hn_2 will be 197 as in the first example.
# Problematic Enumeration Districts for House Number Cleaning
mn_output %>% filter(flag_hn_cleaned == 1) %>%
select(ED) %>% table() %>% sort(decreasing = TRUE) %>% head(24)
## .
## 0190 0984 0805 0843 0094 1267 0967 0212 0024 0586 1271 0403 1269 1073 0509 0256
## 634 289 253 244 191 187 177 172 144 132 102 101 85 81 79 74
## 1036 0681 1170 0786 0924 0442 0638 0768
## 73 63 63 56 56 52 51 51
Out of all the house hold number entries, 364,730 entries were filled in via the Fill Down function 05_House_Number_Fill_Down - 65.4%.
From the sample dataset, an output of 371,833 rows by 33 columns was derived.
There were 1,106 Enumeration Districts, 1,527 microfilms and 7,076 unique street names within the dataset.
The results from the Street Matching function are as follow:
In contrast to Manhattan, there is a larger proportion of Perfect Matches.
# Distribution of Result type in Brooklyn
plot(table(bk_output$result_type),
type = "h",
col = c("blue", "red", "orange", "purple", "green", "pink"),
lwd = 10,
main = "Result Type for Brooklyn",
ylab = "Count",
xlab = "Result Type")
Out of the problematic Street Matches (result type 5 and type 6), there is a trend of some EDs being more problematic than others, i.e. more entries of 5 or 6 within certain Enumeration Districts.
# Problematic EDs for Result Type 5
# Treshold set to 50 (arbitrarily decided)
bk_output %>% filter(result_type == 5) %>%
select(ED) %>% table() %>% sort(decreasing = TRUE) %>% head(27)
## .
## 1007 0519 0573 0147 0552 1066 0150 0120 1412 0331 1038 0477 0491 0562 0905 0926
## 401 203 198 171 149 147 137 106 103 101 82 78 75 73 63 63
## 1028 0559 0580 1026 0807 1408 0935 0154 0987 0365 1000
## 62 61 61 61 59 57 55 54 54 52 50
# Problematic EDs for Result Type 6
# Treshold set to 50 (arbitrarily decided)
bk_output %>% filter(result_type == 6) %>%
select(ED) %>% table() %>% sort(decreasing = TRUE) %>% head(14)
## .
## 0915 0123 0608 0161 0058 1092 0253 0173 0909 0124 0547 0488 0733 0158
## 252 131 110 99 88 88 83 74 69 63 62 60 56 55
# Average result_type by ED
average_result_type_bk <- bk_output %>%
select(ED, result_type) %>%
group_by(ED) %>%
summarise(mean_result_type = mean(result_type))
average_result_type_bk$ED <- as.numeric(average_result_type_bk$ED)
bk_result_type_plot <- ggplot(average_result_type_bk,
aes(x = ED, y = mean_result_type)) +
theme_classic() +
geom_point() +
labs(x = "ED", y = "Average Result Type", title = "Mean Result Type by ED (BK)"
)
ggplotly(bk_result_type_plot)
# Standard Deviation of result_type by ED
sd_result_type_bk <- bk_output %>%
select(ED, result_type) %>%
group_by(ED) %>%
summarise(sd_result_type = sd(result_type))
sd_result_type_bk$ED <- as.numeric(sd_result_type_bk$ED)
bk_sd_result_type_plot <- ggplot(sd_result_type_bk,
aes(x = ED, y = sd_result_type)) +
theme_classic() +
geom_point() +
labs(x = "ED", y = "Standard Deviation of Result Type", title = "Standard Deviation of Result Type by ED (BK)"
)
ggplotly(bk_sd_result_type_plot)
Out of the 76.5% of Perfect Matches (284k entries) 1,005 entries were matched via the Fill Down function 03_Matched_Street_Fill_Down - A much smaller percentage than Manhattan. This suggests that the recording of entries might be more accurate in Brooklynn or that the street directory is more well-developed. Nevertheless, errors are still present.
Out of 371,833 entries, 179,303 entries were flagged for house number changes (i.e. 0 or 1) - 48.2%.
2,567 entries were flagged with “1” meaning the house number was editted by the function. Brooklyn also has several abnormal initial house numbers. E.g. “222 1/2”, “192 3TH”. Otherwise, the issues are similar to Manhattan.
# Problematic Enumeration Districts for House Number Cleaning
bk_output %>% filter(flag_hn_cleaned == 1) %>%
select(ED) %>% table() %>% sort(decreasing = TRUE) %>% head(11)
## .
## 0614 0016 0013 0456 0321 0518 0029 0358 0003 0239 0419
## 322 156 145 109 103 102 80 80 77 77 68
Out of all the house hold number entries, 182,935 entries were filled in via the Fill Down function 05_House_Number_Fill_Down - 49.1%.
# Dan's function - Change ED's factor format to numeric
as.numeric.factor <- function(x) {as.numeric(levels(x))[x]}
# Aggregate the mean of result type, i.e. street matching success rate
# The lower the better
mn_output_result_type <- aggregate(result_type ~ ED, FUN = mean, data = mn_output)
mn_output_result_type$ED <- as.numeric(mn_output_result_type$ED)
# Change Manhattan Shapefile ED variable to numeric format (Dan's function)
mn_map@data$ED <- as.numeric.factor(mn_map@data$ED)
# Left join the 2 datasets
mn_map@data <- left_join(mn_map@data, mn_output_result_type, by = c('ED' = 'ED'))
# Map for Result Type in Manhattan
tmap_mode("view")
## tmap mode set to interactive viewing
tm_shape(mn_map) + tm_polygons("result_type")
## Linking to GEOS 3.7.2, GDAL 2.4.2, PROJ 5.2.0
# Aggregate the mean of result type, i.e. street matching success rate
# The lower the better
bk_output_result_type <- aggregate(result_type ~ ED, FUN = mean, data = bk_output)
bk_output_result_type$ED <- as.numeric(bk_output_result_type$ED)
# Change Brooklyn Shapefile ED variable to numeric format
bk_map@data$ED <- as.numeric.factor(bk_map@data$ED)
# Left join the 2 datasets
bk_map@data <- left_join(bk_map@data, bk_output_result_type, by = c('ED' = 'ED'))
# Map for Result Type in Brooklyn
tmap_mode("view")
## tmap mode set to interactive viewing
tm_shape(bk_map) + tm_polygons("result_type")
# Highlight 2 examples in Brooklyn with poor street name matches
bk_output %>% filter(ED == 1034 | ED == 1039)
# Missing EDs in Manhattan
# Manhattan ED list
mn_ed_list <- mn_map@data$ED %>% sort()
mn_output_ed <- mn_output$ED %>% unique() %>% as.numeric() %>% sort()
# Which EDs are in the Shapefile but missing from the Manhattan dataset
mn_ed_list[!(mn_output_ed %in% mn_ed_list)]
## [1] 43 47 1431 1519 1742 1743 1745
length(mn_ed_list[!(mn_output_ed %in% mn_ed_list)])
## [1] 7
# Which EDs are in the dataset but missing from the Manhattan shapefile
mn_output_ed[!(mn_ed_list %in% mn_output_ed)]
## [1] 699 1070 1175 1712 1727 1737 1745 1747 1748
length(mn_output_ed[!(mn_ed_list %in% mn_output_ed)])
## [1] 9
# Missing EDs in Brooklyn
# Brooklyn ED list
bk_ed_list <- bk_map@data$ED %>% sort()
# Brooklyn Output EDs
bk_output_ed <- bk_output$ED %>% unique() %>% as.numeric() %>% sort()
# Which EDs are in the Shapefile but missing from the Brooklyn dataset
bk_ed_list[!(bk_output_ed %in% bk_ed_list)]
## [1] 512
length(bk_ed_list[!(bk_output_ed %in% bk_ed_list)])
## [1] 1
# Which EDs are in the dataset but missing from the Brooklyn shapefile
bk_output_ed[!(bk_ed_list %in% bk_output_ed)]
## [1] 761 950 958 963 964 1040 1086
length(bk_output_ed[!(bk_ed_list %in% bk_output_ed)])
## [1] 7
There are 38,174 entries with NA best_match. Their street_add entries are also NA. There are 792 EDs with these problems.
na_mn_output <- mn_output %>% filter(is.na(best_match)) %>% select(ED) %>% unique()
na_mn_output <- na_mn_output %>% mutate('NA best match' = 1)
na_mn_map <- merge(mn_map, na_mn_output, 'ED', 'ED')
# Map for NA Street Address / Best Match in Manhattan
tmap_mode("view")
## tmap mode set to interactive viewing
tm_shape(na_mn_map) + tm_polygons("NA best match")
# Another problematic best match is "0"
mn_output %>% filter(best_match == 0) %>% select(street_add) %>% unique()
There are 9,264 entries with NA best_match. Their street_add entries are also NA. There are 427 EDs with these problems.
na_bk_output <- bk_output %>% filter(is.na(best_match)) %>% select(ED) %>% unique()
na_bk_output <- na_bk_output %>% mutate('NA best match' = 1)
na_bk_map <- merge(bk_map, na_bk_output, 'ED', 'ED')
# Map for NA Street Address / Best Match in Brooklyn
tmap_mode("view")
## tmap mode set to interactive viewing
tm_shape(na_bk_map) + tm_polygons("NA best match")
# Another problematic best match is "0"
bk_output %>% filter(best_match == 0) %>% select(street_add) %>% unique()